Different dynamical patterns of the tumor suppressor protein p53 alter the selection and timing of gene expression and affect cellular outcomes in response to DNA damage.
Purvis JE, Karhohs KW, Batchelor E, Loewer A, Lahav G. p53 dynamics control cell fate. (2012) Science 336(6087):1440-4. PMID: 22700930
Cellular Information Processing
Cells send and receive information by controlling the temporal patterns of their signaling molecules. These signaling dynamics influence cellular outcomes and are both shaped and interpreted by the structure of molecular networks.
Purvis JE, Lahav G. Encoding and decoding cellular information through signaling dynamics. (2013) Cell 152(5):945-56. PMID: 23452846
Cells in vivo are controlled by multiple stimuli acting simultaneously. A machine learning algorithm trained with measurements of pairwise stimuli can predict combinations of many signaling inputs.
Chatterjee MS, Purvis JE, Brass LF, Diamond SL. Pairwise agonist scanning predicts cellular signaling responses to combinatorial stimuli. (2010) Nature Biotechnology 28(7):727-32. PMID: 20562863
Reverse Engineering the Human Platelet
A computational model of the human platelet provides a surprisingly simple explanation for why individual platelets show sporadic bursts of calcium: the model predicts that this behavior is due simply to the extremely small volume of the platelet cytoplasm―about 6 femtoliters―which gives rise to stochastic fluctuations of key signaling molecules.
Purvis JE, Chatterjee MS, Brass LF, Diamond SL. A molecular signaling model of platelet phosphoinositide and calcium regulation during homeostasis and P2Y1 activation. (2008) Blood112(10):4069-79. PMID: 18596227